zs_min = min(sn_density[:,0]) zs_max = max(sn_density[:,0]) zp_min = zs_min zp_max = zs_max A = loadtxt('../mu_zs2zp.txt') nrow, ncol = A.shape zs = linspace(zs_min, zs_max, ncol+1) zs = (zs[1:]+zs[:-1])/2.0 muzs = zeros(zs.shape) for i in range(len(zs)): muzs[i] = SC.dist_mu(zs[i]) zp = linspace(zp_min, zp_max, nrow) muzp = matmul(A,muzs) #plot(zp, muzp, '-r', label='photo-z') #plot(zs, muzs, '-g', label='spec-z') #legend(loc='best') #show() cnt = 0 sample_zp = [] sample_muzp = [] sample_muzperr = []
SC = SNeCosmology(H0=67.7, Omega_m=0.307, Omega_k=0.0, w0=-1.0, wa=0.0, z_sn_max=1.5, grid_size=100) NumSNe = 20000 NullzErr = 1.0 sn_z = [] sn_mu= [] sn_dmu = [] sn_zerr= [] cnt = 0 while cnt < NumSNe: zi = get_rand_z() zerr = 0.02*(1.+zi)*randn()*NullzErr if zi + zerr > 0.01 and zi + zerr <=1.299: mui = SC.dist_mu(zi) dmui = fun_dmu(zi) dmui = 0.12 sn_z.append(zi+zerr) sn_mu.append(mui+dmui*randn()) sn_dmu.append(dmui) sn_zerr.append(0.02*(1.+zi)) cnt += 1 sn_z = array(sn_z) sn_mu = array(sn_mu) sn_dmu = array(sn_dmu) sn_zerr= array(sn_zerr) savetxt('JLA_mock.txt', hstack((sn_z.reshape(NumSNe,1), sn_mu.reshape(NumSNe,1), sn_dmu.reshape(NumSNe,1), sn_zerr.reshape(NumSNe,1))), fmt='%15.4f', delimiter=' ')